Abstract 5423: AI powered quantification of mitotic rate in H&amp;E stained tissue detects significant differences between treatment groups of preclinical pancreas cancer xenografts
Authors: Sharon Ruane, Lukas Ruff, Brian Reichholf, Christina Aigner, Emil Barbuta, Stephan Tietz, Olivér Atanaszov, Rosemarie Krupar, Simon Schallenberg, Maximilian Alber, Francesca Trapani, Frederick Klauschen
Journal: Cancer Research (2023)

Abstract Background: Mitotic rate is a readout routinely used for characterization of tumor samples. Standard methods to quantify cell division include manual pathologist counts based on hematoxylin and eosin (H&amp;E) staining and either manual or automated assessment of immunohistochemistry (IHC) staining using phospho-histone H3 (pHH3). These suffer the drawbacks of high inter-pathologist variability in the case of H&amp;E assessment and time inefficiency and false positive calls in case of pHH3 staining. Aiming to overcome these issues, we developed a mitosis detection model based on H&amp;E-stained tissue sections alone. Methods: To develop and evaluate the model, we used 1032 H&amp;E-stained tissue sections (156 used for model training) of pre-clinical pancreas cancer xenografts originating from mice that have undergone a series of experiments conducted to examine the pharmacodynamic effect of several anti-cancer protocols. We trained (i) a tissue segmentation model (segmenting tissue regions into ‘carcinoma’, ‘stroma’, ‘necrosis’, and ‘other’) and (ii) a segmentation model for pixel-level mitosis detection (segmenting ‘mitosis’ vs. ‘non-mitosis’). Regions predicted as mitosis were post-processed to represent individual dividing cells. The tissue segmentation model served as a filter to predict mitotic rate for carcinoma tissue areas only, which has not been accounted for in previous AI-based methods for mitotic rate prediction on H&amp;E tissue. To evaluate the model on detecting mitotic events, the model was compared against a 5-pathologist consensus of mitotic count. The mitotic rates predicted by the model were used to infer differences between treatment groups (various treatments and dosages vs. control). Results: The mitosis detection model for quantifying rates of cell division in carcinoma regions of H&amp;E-stained tissue sections showed a notable agreement with the 5-pathologist mitotic count consensus (Pearson correlation 0.92). Furthermore, the model correlated well with mitosis counts based on pHH3 IHC staining (Pearson correlation 0.78), which were available for 63 cases. Finally, when used to characterize the entire CDX cohort, the case level mitotic rate predicted by the model showed significant differences between treatment groups in line with or better than using a pHH3 IHC stain. Conclusion: This study demonstrates the efficacy and scalability of AI-based models for the quantification of mitotic rate based on H&amp;E-stained tissue alone, presenting a time- and cost-efficient approach that also mitigates inter-annotator variability. Citation Format: Sharon Ruane, Lukas Ruff, Brian Reichholf, Christina Aigner, Emil Barbuta, Stephan Tietz, Olivér Atanaszov, Rosemarie Krupar, Simon Schallenberg, Maximilian Alber, Francesca Trapani, Frederick Klauschen. AI powered quantification of mitotic rate in H&amp;E stained tissue detects significant differences between treatment groups of preclinical pancreas cancer xenografts. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5423.